The communications industry is rapidly changing to adjust to emerging technologies and ever increasing customer demand. This customer demand for new applications and increased performance of existing applications is driving communications network and system providers to employ networks and systems having greater speed and capacity (e.g., greater bandwidth). In trying to achieve these goals, a common approach taken by many communications providers is to use packet switching technology. Increasingly, public and private communications networks are being built and expanded using various packet technologies, such as Internet Protocol (IP). Note, nothing described or referenced in this document is admitted as prior art to this application unless explicitly so stated.
A network device, such as a switch or router, typically receives, processes, and forwards or discards a packet. For example, an enqueuing component of such a device receives a stream of various sized packets which are accumulated in an input buffer. Each packet is analyzed, and an appropriate amount of memory space is allocated to store the packet. The packet is stored in memory, while certain attributes (e.g., destination information and other information typically derived from a packet header or other source) are maintained in separate memory. Once the entire packet is written into memory, the packet becomes eligible for processing, and an indicator of the packet is typically placed in an appropriate destination queue for being serviced according to some scheduling methodology.
When there is a contention for resources, such as on output links of a packet switching system or interface or even for compute cycles in a computing device, it is important for resources to be allocated or scheduled according to some priority and/or fairness policy. Moreover, the amount of work required to schedule and to enqueue and dequeue a packet or other scheduled item is important, especially as the operating rate of systems increase. Many different mechanisms have been used by an individual schedule to schedule packets, many of which are described hereinafter.
Ordinary time division multiplexing (TDM) is a method commonly used for sharing a common resource between several clients. All scheduled clients are served one at a time at predetermined times and for pre-allocated time periods, which is a very useful property for many applications. This method is often used for multiplexing multiple synchronous items over a higher speed communications link, such as that used for multiplexing multiple telephone calls over a single facility or interleaving packets. However, in a dynamic environment wherein items may not require the full amount of their allocated time slot such as when an item may only require none or only a portion of a particular allocated time slot, then bandwidth of the resource is typically wasted.
Ordinary round-robin (RR) is another method commonly used for sharing a common resource between several clients. All clients are served in a cyclic order. In each round every client will be served if it is eligible. When served, each client is permitted to send one packet. Servicing of queues is simple to implement and can be done in constant time, but, due to the varying size of packets, does not allocate bandwidth fairly. For example, certain higher priority or larger bandwidth ports or streams of packets may not get their desired amount of bandwidth, which may especially be the case when serving one large and numerous smaller traffic streams or when different priorities of traffic are scheduled.
In some scenarios, high priority (e.g., low latency), guaranteed bandwidth, best effort traffic (e.g., spare bandwidth) and other classifications of traffic compete for a common resource. Various known scheduling methods are designed to provide isolation, prioritization, and fair bandwidth allocation to traffic competing for a common resource. These are known as fair queuing methods. Some examples are Weighted Fair Queuing (WFQ), Self-Clocked Fair Queuing (SCFQ), and Deficit Round Robin/Surplus Round Robin (referred to as DRR).
WFQ and SCFQ depend upon arrival times as well as previous link utilization in order to calculate the next best packet to send. The accepted “ideal” behavior is bit-by-bit or weighted bit-by-bit round robin which assigns each bit of each packet in the system an ideal finish time according to the weighted fair sharing of the system. This is typically not practical in a packet-based system unless all packets are one bit. Generalizing the algorithm from bit-by-bit to packet-by-packet, each packet is assigned an ideal finish (departure) time and the packets are served in order of the earliest departure time. The inclusion of theoretical departure times in a scheduling method typically requires insertion into a sorted list which is known to be an O(log N) problem implemented in software, where N is typically the number of queues. In hardware, this problem may be reduced to an O(1) operation with O(log N) resources.
DRR is a method used for sharing a common resource between several clients with different ratios between clients (i.e., some clients are allowed to consume more of the resources than others). The ratio between clients is typically defined by a parameter called a quantum. There are many variations and different implementations of DRR, including that described hereinafter.
DRR services queues using round-robin servicing with a quantum assigned to each queue. Unlike traditional round-robin, multiple packets up to the specified quantum can be sent resulting in each queue sending at least a quantum's worth of bytes. If the quantum for each queue is equal, then each queue will consume an equal amount of bandwidth.
This DRR approach works in rounds, where a round is one round-robin iteration over the queues that have items to be sent. Typically, when the queue is scheduled, it is allowed to transmit until its deficit becomes negative (or non-positive), and then the next queue is served. Packets coming in on different flows are stored in different queues. Each round, each queue is allocated a quantum worth of bytes, which are added to the deficit of each queue. Each queue is allowed to send out one or more packets in a DRR round, with the exact number of packets being sent in a round being dependent on its quantum and the size of the packets being sent. Typically, as long as the deficit is a positive (or non-negative) value (i.e., it is authorized to send a packet) in a DRR round for a queue and it has one or more packets to send, a packet is sent and its deficit is reduced based on the size of the sent packet. If there are no more packets in a queue after the queue has been serviced, one implementation sets the deficit corresponding to the queue to zero, while one implementation does this only if its deficit is negative. Otherwise, the remaining amount (i.e., the deficit minus the number of bytes sent) is maintained for the next DRR round.
DRR has a complexity of O(1)—that is the amount of work required is a constant and independent of the number of packets enqueued. In order to be work conserving, a packet should be sent every time a queue is scheduled no matter its size. Thus, the quantum used in DRR should be at least one maximum packet size (MTU) to guarantee that when the quantum is added to any deficit, the resulting value is at least zero. DRR provides fair bandwidth allocation and is easy to implement. It is work conserving and, because of its O(1) properties, it scales well with higher link speeds and larger number of queues. However, its scheduling behavior deviates quite a bit from the bit-by-bit round robin “ideal.” In particular, latency for a system with N queues is Q*N where Q is the average quantum, which must be at least one maximum transmission unit (MTU).
These scheduling techniques can work well for scheduling a single layer of service or traffic. However, bandwidth is being sold to end customers based on types and aggregation of traffic. For example, customers might subscribe to certain types of traffic with different delay and bandwidth requirements, such as voice, video, gaming, email, instant messaging, and Internet browsing. Some of these traffic types can be very time and delay sensitive, while other types of traffic can be serviced using a best effort without too much impact on the service. Thus, service providers may be able to sell a premium service which provides a minimum guaranteed service rate for specific one or more types of traffic, while providing best effort service for its remaining types of traffic.
However, the scheduling of multiple types of traffic with guaranteed minimum scheduling rates intermixed with other traffic can be complex, especially when each of these layers of services are aggregated with other end users and possibly other customers, and especially in the context of the dynamic traffic, such as the intermittent, partial and/or full use of subscribed services and changing types of traffic and number of sources. For example, the guaranteed rate traffic and best effort traffic may be aggregated on a digital subscriber line (DSL) or as part of a virtual LAN (VLAN), with each of these layers of service typically having their own service requirements. If a policer function is used to limit the traffic rate for a maximum subscribed rate, then packets are typically dropped or service backpressured. However, indiscriminate dropping of packets or throttling of all types of traffic can especially impact services which are delay and bandwidth sensitive. Also, traffic with guaranteed service rates needs to be serviced accordingly, while not allowing one source to consume its guaranteed service rate along with the guaranteed service rate of others which is not currently being used by them. Needed are new ways to accommodate different types and aggregations of traffic.